Localization information is increasingly being leveraged for various location-based services (e.g., navigation, mobile commerce, etc.). These location-based services utilize information pertaining to the location of a mobile device to enable a multitude of computing applications. Often, the location of a mobile device may be obtained through the use of the existing global positioning system (GPS) (i.e., GPS satellites) due to the fact that most mobile devices are equipped with GPS receivers.
However, in certain environments (e.g., indoor environments), GPS signals are unavailable. Buildings and similar objects that obstruct GPS signals often lead to the unavailability of GPS signals used for localization. This has led to research efforts on localization for mobile devices using other, non-GPS approaches. At least one approach is to leverage the existing infrastructure of WiFi access points to enable localization based on available radio-frequency (RF) signals in lieu of the unavailable GPS signals. The WiFi infrastructure is widely deployed infrastructure and therefore suitable for localization due to the locality preserving properties of WiFi signals.
There are generally two techniques used for WiFi-based localization: (1) fingerprint-based localization, and (2) model-based localization. Fingerprint-based localization infers location of a device by comparing an observed WiFi sample against a location database, which contains a number of collected WiFi samples and their associated positions. The WiFi sample(s) that best matches the signal query is used for localization. However, fingerprint-based localization requires an extensive and costly pre-deployment effort to build the location database with enough training samples for accurate localization.
Model-based localization, on the other hand, does not rely too heavily on the density of training samples. Accordingly, the number of training samples used for model-based localization may be reduced significantly compared to fingerprint-based localization methods, which results in a much cheaper system. Model-based localization works by using a signal propagation model (e.g., a log-distance path loss (LDPL) model) of a WiFi signal to obtain model parameters of WiFi access points (APs) for predicting received signal strength (RSS) at various locations within an area. Thereafter, a location query with certain WiFi observations may be resolved to a location that best fits the WiFi observations to the signal propagation model.
While the model-based localization approach significantly reduces the pre-deployment effort and the associated cost of the system as compared to fingerprint-based localization, existing model-based approaches utilize a single (“global”) model for localization within an entire area (e.g., an indoor environment) for each AP. A global path loss model uses a single path loss constant to reflect the assumption that RSS should decrease uniformly with increasing distance from a given WiFi AP. However, due to the complexity of many environments (e.g., walls, cubicles, pedestrians, etc.) that may all affect WiFi signal propagation, this assumption is not true of many environments, leading to uneven model fitness across different sub-areas of an environment having complex properties. In other words, model-based localization using a global signal propagation model for an entire area is oversimplified, leading to suboptimal performance of model-based localization systems.